This project provides a web-interface, as well as a programmatic-api for various machine learning algorithms. Some of it's general applications, have been outlined within index.rst.

Supported algorithms:

• Support Vector Machine (SVM)
• Support Vector Regression (SVR)

Please adhere to contributing.md, when contributing code. Pull requests that deviate from the contributing.md, could be labelled as invalid, and closed (without merging to master). These best practices will ensure integrity, when revisions of code, or issues need to be reviewed.

Note: support, and philantropy can be inquired, to further assist with development.

Fork this project, and remember to generate ssh keys, before cloning the repository:

• simple clone: clone the remote master branch.
• commit hash: clone the remote master branch, then checkout a specific commit hash.
• release tag: clone the remote branch, associated with the desired release tag.

Note: the puppetfile.rst can be reviewed, to better understand why ssh keys are required.

In order to proceed with the installation for this project, two dependencies need to be installed:

• Vagrant
• Virtualbox (with extension pack)

Once the necessary dependencies have been installed, execute the following command to build the virtual environment:

cd /path/to/machine-learning/
vagrant up

Depending on the network speed, the build can take between 10-15 minutes. So, grab a cup of coffee, and perhaps enjoy a danish while the virtual machine builds. Remember, the application is intended to run on localhost, where the Vagrantfile defines the exact port-forward on the host machine.

Note: a more complete refresher on virtualization, can be found within the vagrant wiki page.

The following lines, indicate the application is accessible via localhost:8080, on the host machine:

...
  ## Create a forwarded port mapping which allows access to a specific port
  #  within the machine from a port on the host machine. In the example below,
  #  accessing "localhost:8080" will access port 80 on the guest machine.
  config.vm.network "forwarded_port", guest: 5000, host: 8080
...

Note: ssl is configured on the reverse proxy, such that accessing http://localhost:8080, on the host machine, will redirect to https://localhost:8080.

Both the web-interface, and the programmatic-api, have corresponding unit tests which can be reviewed, and implemented.

The web-interface , or GUI implementation, allow users to implement the following sessions:

• data_new: store the provided dataset(s), within the implemented sql database.
• data_append: append additional dataset(s), to an existing representation (from an earlier data_new session), within the implemented sql database.
• model_generate: using previous stored dataset(s) (from an earlier
• data_new, or data_append session), generate a corresponding model into
• model_predict: using a previous stored model (from an earlier model_predict session), from the implemented nosql datastore, along with user supplied values, generate a corresponding prediction.

When using the web-interface, it is important to ensure the csv, xml, or json file(s), representing the corresponding dataset(s), are properly formatted. Dataset(s) poorly formatted will fail to create respective json dataset representation(s). Subsequently, the dataset(s) will not succeed being stored into corresponding database tables; therefore, no model, or prediction can be made.

The following are acceptable syntax:

• CSV sample datasets
• XML sample datasets
• JSON sample datasets

Note: each dependent variable value (for JSON datasets), is an array (square brackets), since each dependent variable may have multiple observations.

As mentioned earlier, the web application can be accessed after subsequent vagrant up command, followed by using a browser referencing localhost:8080, on the host machine.

The programmatic-interface, or set of API, allow users to implement the following sessions:

• data_new: store the provided dataset(s), within the implemented sql database.
• data_append: append additional dataset(s), to an existing representation (from an earlier data_new session), within the implemented sql database.
• model_generate: using previous stored dataset(s) (from an earlier
• data_new, or data_append session), generate a corresponding model into
• model_predict: using a previous stored model (from an earlier model_predict session), from the implemented nosql datastore, along with user supplied values, generate a corresponding prediction.

A post request, can be implemented in python, as follows:

import requests

endpoint_url = 'https://localhost:8080/load-data'
headers = {'Content-Type': 'application/json'}

requests.post(endpoint_url, headers=headers, data=json_string_here)

Note: the above post request, can be implemented in a different language, respectively.

Note: various data attributes can be nested in above POST request.